code: def find_neighbours_on_rect_grid(grid, center, radius):
      """
      Function find all neuron's neighbours around specified
      center within a certain radius.
  
      Parameters
      ----------
      {find_neighbour_distance.grid}
  
      {find_neighbour_distance.center}
  
      radius : int
          Radius specifies what neurons, around the center, are neighbours.
  
      Returns
      -------
      array-like
          Return matrix with the same dimension as ``grid``
          where center element and it neighbours positions
          filled with value ``1`` and other as a ``0`` value.
  
      Examples
      --------
      >>> import numpy as np
      >>> from neupy.algorithms.competitive import sofm
      >>>
      >>> sofm.find_neighbours_on_rect_grid(
      ...     grid=np.zeros((3, 3)),
      ...     center=(0, 0),
      ...     radius=1)
      ...
      array([[ 1.,  1.,  0.],
             [ 1.,  0.,  0.],
             [ 0.,  0.,  0.]])
      >>>
      >>> sofm.find_neighbours_on_rect_grid(
      ...     grid=np.zeros((5, 5)),
      ...     center=(2, 2),
      ...     radius=2)
      ...
      array([[ 0.,  0.,  1.,  0.,  0.],
             [ 0.,  1.,  1.,  1.,  0.],
             [ 1.,  1.,  1.,  1.,  1.],
             [ 0.,  1.,  1.,  1.,  0.],
             [ 0.,  0.,  1.,  0.,  0.]])
      """
      distances = find_neighbour_distance(grid, center)
      mask = sum(dist**2 for dist in distances) <= radius**2
      grid[mask] = 1
      return grid
  
file_path: neupy/neupy/algorithms/competitive/neighbours.py
imports: from __future__ import division
  import numpy
  from neupy.core.docs import shared_docs
label_desc: Use image node_id 4 for calling a global function with example usage: find_neighbours_on_rect_grid(grid, center, radius) and returns: grid